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Semi-supervised kernel regression using whitened function classes

MPS-Authors
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Franz,  MO
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

/persons/resource/persons84032

Kwon,  Y
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

/persons/resource/persons84156

Rasmussen,  CE
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

/persons/resource/persons84193

Schölkopf,  B
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

External Resource

https://link.springer.com/content/pdf/10.1007%2F978-3-540-28649-3_3.pdf
(Publisher version)

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Citation

Franz, M., Kwon, Y., Rasmussen, C., & Schölkopf, B. (2004). Semi-supervised kernel regression using whitened function classes. In C. Rasmussen, H. Bülthoff, B. Schölkopf, & M. Giese (Eds.), Pattern Recognition: 26th DAGM Symposium, Tübingen, Germany, August 30 - September 1, 2004 (pp. 18-26). Berlin, Germany: Springer.


Cite as: https://hdl.handle.net/11858/00-001M-0000-0013-F37A-B
Abstract
The use of non-orthonormal basis functions in ridge regression leads to an often undesired non-isotropic prior in function space. In this
study, we investigate an alternative regularization technique that
results in an implicit whitening of the basis functions by penalizing
directions in function space with a large prior variance. The
regularization term is computed from unlabelled input data that
characterizes the input distribution. Tests on two datasets using
polynomial basis functions showed an improved average performance
compared to standard ridge regression.